90 citations · 331 across the 28 of their papers we have counts for
10 papers · 1 filter
SPIDES: Information-Flow Control for Browser Agents
Egor Zverev, Juhee Kim, Leon Trampert +4
Browser agents automate complex workflows by reading and acting on arbitrary page content, but this flexibility exposes them to prompt injection attacks and unintended data exfiltr…
Optimizing Agent Planning for Security and Autonomy
Aashish Kolluri, Rishi Sharma, Manuel Costa +5
Indirect prompt injection attacks threaten AI agents that execute consequential actions, motivating deterministic system-level defenses. Such defenses can provably block unsafe act…
Securing AI Agents with Information-Flow Control
Manuel Costa, Boris Köpf, Aashish Kolluri +6
As AI agents become increasingly autonomous and capable, ensuring their security against vulnerabilities such as prompt injection becomes critical. This paper explores the use of i…
Closed-Form Bounds for DP-SGD against Record-level Inference
Giovanni Cherubin, Boris Köpf, Andrew Paverd +3
Machine learning models trained with differentially-private (DP) algorithms such as DP-SGD enjoy resilience against a wide range of privacy attacks. Although it is possible to deri…
Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting
Ana-Maria Cretu, Daniel Jones, Yves-Alexandre de Montjoye +1
Machine learning models have been shown to leak sensitive information about their training datasets. Models are increasingly deployed on devices, raising concerns that white-box ac…
Distribution inference risks: Identifying and mitigating sources of leakage
Valentin Hartmann, Léo Meynent, Maxime Peyrard +3
A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inferen…
MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models
Yixi Xu, Sumit Mukherjee, Xiyang Liu +3
Generative machine learning models are being increasingly viewed as a way to share sensitive data between institutions. While there has been work on developing differentially priva…
SOTERIA: In Search of Efficient Neural Networks for Private Inference
Anshul Aggarwal, Trevor E. Carlson, Reza Shokri +1
ML-as-a-service is gaining popularity where a cloud server hosts a trained model and offers prediction (inference) service to users. In this setting, our objective is to protect th…
FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning
Sameer Wagh, Shruti Tople, Fabrice Benhamouda +3
We propose Falcon, an end-to-end 3-party protocol for efficient private training and inference of large machine learning models. Falcon presents four main advantages - (i) It is hi…
Privado: Practical and Secure DNN Inference with Enclaves
Karan Grover, Shruti Tople, Shweta Shinde +2
Cloud providers are extending support for trusted hardware primitives such as Intel SGX. Simultaneously, the field of deep learning is seeing enormous innovation as well as an incr…